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Load in Honolii Sea Surface Temperature Data using here package
sst_honolii_1985_2021 <- read.csv(here("data", "sst_honolii_1985_2021.csv" ))
sst_honolii_1985_2021Clean Data
sst_honolii_1985_2021 <- sst_honolii_1985_2021 %>%
select(time, CRW_SST) %>%
mutate(time = as.Date(time))
sst_honolii_1985_2021Determine SST Monthly Means
sst_honolii_1985_2021_month <- sst_honolii_1985_2021 %>%
mutate(time = floor_date(time, "month")) %>%
group_by(time) %>%
summarise(mean_sst = mean(CRW_SST))
sst_honolii_1985_2021_monthPlot Data
sst_honolii_1985_2021_month_plot <- ggplot(data = sst_honolii_1985_2021_month, aes(x = time, y = mean_sst)) +
geom_line()
sst_honolii_1985_2021_month_plot
Look for trend using a linear regression
sst_honolii_1985_2021_month_regression <- sst_honolii_1985_2021_month_plot +
geom_smooth(method="lm", col="black")
sst_honolii_1985_2021_month_regression## `geom_smooth()` using formula 'y ~ x'
Convert dataframe into a time series
sst_honolii_1985_2021_month_ts <- ts(sst_honolii_1985_2021_month$mean_sst, start = c(1985, 11), frequency = 12)
sst_honolii_1985_2021_month_tsRun a classical decomposition model
as_tsibble(sst_honolii_1985_2021_month_ts) %>%
model(
classical_decomposition(value, type = "additive")
) %>%
components() %>%
autoplot() +
labs(title = "Classical additive decomposition")## Warning: Removed 6 row(s) containing missing values (geom_path).
sst_honolii_1985_2021_month_tsibble <- sst_honolii_1985_2021_month_ts %>% as_tsibble()
sst_honolii_1985_2021_month_tsibblesst_honolii_1985_2021_month_tsibble %>%
gg_season(value)
sst_honolii_1985_2021_month_tsibble %>%
gg_subseries(value)
dcmp <- sst_honolii_1985_2021_month_tsibble %>%
model(stl = STL(value))
components(dcmp)components(dcmp) %>%
as_tsibble() %>%
autoplot(value, colour="gray") +
geom_line(aes(y=trend), colour = "#D55E00")
Forecasting
DSHW
training=window(sst_honolii_1985_2021_month_ts, start = c(1985,11), end = c(2018,11))
validation=window(sst_honolii_1985_2021_month_ts, start = c(2018,11), end = c(2021,10))dshw_model = dshw(training, period1=4, period2 = 12, h=length(validation))
MAPE(dshw_model$mean, validation)*100## [1] 10178.31dshw_model## Jan Feb Mar Apr May Jun Jul Aug
## 2018
## 2019 25.08711 24.96195 24.81938 25.14442 25.60915 26.03291 26.50949 26.95839
## 2020 25.14732 25.03537 24.89699 25.22464 25.69140 26.11670 26.59485 27.04519
## 2021 25.22819 25.11586 24.97702 25.30570 25.77393 26.20057 26.68024 27.13201
## Sep Oct Nov Dec
## 2018 25.68476
## 2019 27.31078 27.06246 26.49765 25.70703
## 2020 27.39870 27.14956 26.58291 25.78973
## 2021 27.48662 27.23666 26.66817dshw_plot <-plot(sst_honolii_1985_2021_month_ts, col="blue", xlab="Day", ylab="SST", main="DSHW Forecast", type='l')
lines(dshw_model$mean, col="red", lwd=2)
dshw_model_2 = dshw(training, period1=12, period2 =72, h = 72)
dshw_model_2## Point Forecast
## 6.514 25.76427
## 6.528 25.27364
## 6.542 25.10713
## 6.556 25.17508
## 6.569 25.48576
## 6.583 26.09498
## 6.597 26.38883
## 6.611 26.84103
## 6.625 27.15326
## 6.639 27.46529
## 6.653 26.97055
## 6.667 26.51511
## 6.681 25.95479
## 6.694 25.41962
## 6.708 25.09870
## 6.722 24.92979
## 6.736 25.28577
## 6.750 25.91912
## 6.764 26.19231
## 6.778 26.54581
## 6.792 26.94157
## 6.806 27.29538
## 6.819 26.96008
## 6.833 26.40583
## 6.847 25.55935
## 6.861 25.11895
## 6.875 25.03843
## 6.889 24.74309
## 6.903 24.99382
## 6.917 25.35678
## 6.931 26.10075
## 6.944 26.54165
## 6.958 27.01197
## 6.972 27.31504
## 6.986 27.01931
## 7.000 26.42241
## 7.014 25.74988
## 7.028 25.21549
## 7.042 25.16228
## 7.056 25.00241
## 7.069 25.39652
## 7.083 25.76707
## 7.097 26.38554
## 7.111 26.95357
## 7.125 27.47323
## 7.139 27.77144
## 7.153 27.57070
## 7.167 27.09244
## 7.181 26.33333
## 7.194 25.65600
## 7.208 25.55392
## 7.222 25.36482
## 7.236 25.51926
## 7.250 25.75743
## 7.264 26.03201
## 7.278 26.56131
## 7.292 26.99442
## 7.306 27.37112
## 7.319 27.19741
## 7.333 26.58739
## 7.347 25.67649
## 7.361 25.04795
## 7.375 24.97376
## 7.389 24.82337
## 7.403 25.26077
## 7.417 25.79227
## 7.431 26.06352
## 7.444 26.51123
## 7.458 27.00339
## 7.472 27.47153
## 7.486 27.30549
## 7.500 26.72441fcst_dshw <- forecast(dshw_model_2, h = 50)
autoplot(fcst_dshw, include = 220)
print(summary(fcst_dshw))##
## Forecast method: DSHW
##
## Model Information:
## $mape
## [1] 0.974642
##
## $mse
## [1] 0.1075704
##
## $alpha
## [1] 0.4152583
##
## $beta
## [1] 1.355498e-05
##
## $gamma
## [1] 0.1224699
##
## $omega
## [1] 0.5139064
##
## $phi
## [1] 0.328233
##
## $lambda
## NULL
##
## $l0
## [1] 25.06396
##
## $b0
## [1] 0.003291031
##
## $s10
## [1] 1.0105218 1.0127200 0.9908013 0.9928881 0.9878664 1.0320809 1.0181183
## [8] 1.0360254 1.0214661 1.0096565 1.0142107 1.0255065 1.0093905 1.0126603
## [15] 0.9807656 0.9926516 0.9752726 1.0091724 0.9907641 0.9970735 1.0014875
## [22] 1.0040303 1.0040312 0.9968292 0.9834281 0.9892821 0.9673074 0.9865870
## [29] 0.9931041 1.0015942 0.9836387 0.9858493 0.9864397 1.0045708 0.9978704
## [36] 0.9750801 1.0090517 1.0287410 1.0065025 1.0081592 1.0049827 1.0148160
## [43] 1.0012875 1.0086382 0.9965232 0.9941782 0.9849620 0.9788491 0.9891431
## [50] 0.9996697 1.0179639 0.9935371 0.9840444 1.0172364 0.9976472 0.9884729
## [57] 0.9784914 1.0039579 0.9964202 0.9840101 0.9892005 0.9997283 0.9884270
## [64] 1.0173002 0.9793638 0.9835771 0.9714636 0.9820825 0.9860554 0.9934826
## [71] 0.9885807 0.9906736
##
## $s20
## [1] 1.0366805 0.9888938 0.9706338 0.9497203 0.9608535 0.9466356 0.9848900
## [8] 1.0008630 1.0273776 1.0432235 1.0569123 1.0534417 1.0354196 0.9885660
## [15] 0.9722768 0.9518994 0.9621649 0.9439154 0.9843132 0.9993591 1.0275475
## [22] 1.0441498 1.0570235 1.0526131 1.0360776 0.9884623 0.9741848 0.9520672
## [29] 0.9635444 0.9426762 0.9826161 0.9989720 1.0273658 1.0450686 1.0575176
## [36] 1.0534106 1.0376789 0.9882854 0.9752810 0.9524879 0.9626981 0.9420859
## [43] 0.9824074 0.9989649 1.0267262 1.0430821 1.0564982 1.0553659 1.0372439
## [50] 0.9881651 0.9748294 0.9523300 0.9626371 0.9424929 0.9824971 0.9989774
## [57] 1.0266454 1.0429878 1.0564497 1.0551677 1.0372216 0.9882354 0.9744984
## [64] 0.9522214 0.9626057 0.9428758 0.9825925 0.9989968 1.0265682 1.0428943
## [71] 1.0563919 1.0549564 1.0371511 0.9882500 0.9741515 0.9520760 0.9625455
## [78] 0.9432147 0.9826662 0.9989871 1.0264653 1.0427941 1.0563526 1.0547758
## [85] 1.0374601 0.9881762 0.9731601 0.9513740 0.9624226 0.9446995 0.9827612
## [92] 0.9992638 1.0260306 1.0422696 1.0562949 1.0548846 1.0371127 0.9883145
## [99] 0.9724143 0.9515253 0.9621685 0.9455824 0.9834378 0.9992471 1.0259667
## [106] 1.0418892 1.0561763 1.0545805 1.0367679 0.9886947 0.9720783 0.9515222
## [113] 0.9625883 0.9459392 0.9835848 0.9993111 1.0262024 1.0424401 1.0561697
## [120] 1.0535578 1.0339752 0.9852328 0.9713823 0.9524234 0.9643402 0.9466860
## [127] 0.9828687 1.0001682 1.0279668 1.0433127 1.0569813 1.0534627 1.0346090
## [134] 0.9869153 0.9708826 0.9538712 0.9630084 0.9463201 0.9841198 1.0016862
## [141] 1.0286865 1.0417052 1.0558063 1.0533709 1.0340270 0.9853113 0.9716249
## [148] 0.9529097 0.9641163 0.9480016 0.9851614 1.0022190 1.0282186 1.0409173
## [155] 1.0553002 1.0523815 1.0319527 0.9838163 0.9712834 0.9527557 0.9643981
## [162] 0.9455506 0.9816763 1.0004189 1.0280257 1.0427906 1.0551050 1.0515696
## [169] 1.0328478 0.9838988 0.9734643 0.9533188 0.9652631 0.9460614 0.9817371
## [176] 0.9997311 1.0266443 1.0410338 1.0540593 1.0513094 1.0334844 0.9851179
## [183] 0.9746688 0.9542277 0.9657807 0.9459158 0.9820376 1.0012166 1.0272911
## [190] 1.0404013 1.0534402 1.0514804 1.0324938 0.9841144 0.9743626 0.9535321
## [197] 0.9646729 0.9458140 0.9821618 1.0009581 1.0278485 1.0414974 1.0550526
## [204] 1.0524117 1.0322580 0.9842401 0.9729791 0.9537750 0.9652088 0.9465709
## [211] 0.9823538 1.0027432 1.0281979 1.0414643 1.0542901 1.0521566 1.0328387
## [218] 0.9848312 0.9733792 0.9525384 0.9650789 0.9476862 0.9830196 1.0034684
## [225] 1.0275281 1.0410107 1.0537551 1.0524508 1.0315754 0.9850061 0.9730378
## [232] 0.9539709 0.9658127 0.9483585 0.9832578 1.0016106 1.0258045 1.0400754
## [239] 1.0534597 1.0509137 1.0312974 0.9856642 0.9741750 0.9554692 0.9655174
## [246] 0.9486325 0.9846300 1.0017682 1.0252702 1.0388999 1.0525930 1.0492355
## [253] 1.0310139 0.9871326 0.9751816 0.9546314 0.9646710 0.9487099 0.9844100
## [260] 1.0027136 1.0246530 1.0383112 1.0527858 1.0501464 1.0309274 0.9863973
## [267] 0.9745068 0.9540410 0.9640169 0.9489149 0.9841252 1.0025391 1.0247498
## [274] 1.0382913 1.0532864 1.0507713 1.0304136 0.9864963 0.9730327 0.9533274
## [281] 0.9656422 0.9485671 0.9839831 1.0022089 1.0270980 1.0370118 1.0526498
## [288] 1.0507042 1.0295856 0.9876132 0.9732266 0.9514549 0.9636843 0.9492494
## [295] 0.9868586 1.0042011 1.0273251 1.0376670 1.0519520 1.0504425 1.0287676
## [302] 0.9881122 0.9743852 0.9517944 0.9616058 0.9483228 0.9851582 1.0023420
## [309] 1.0262337 1.0369728 1.0515722 1.0503794 1.0302117 0.9896339 0.9756735
## [316] 0.9538870 0.9634234 0.9482386 0.9846637 1.0008321 1.0249795 1.0363352
## [323] 1.0505574 1.0497780 1.0302670 0.9888834 0.9764364 0.9552599 0.9624045
## [330] 0.9485402 0.9853572 0.9990219 1.0246023 1.0361563 1.0499982 1.0507147
## [337] 1.0300072 0.9885202 0.9762614 0.9553204 0.9615339 0.9495838 0.9887482
## [344] 0.9991283 1.0241093 1.0366427 1.0508448 1.0511124 1.0305381 0.9891064
## [351] 0.9760594 0.9568971 0.9620408 0.9487978 0.9888420 0.9993625 1.0240081
## [358] 1.0372559 1.0523516 1.0516033 1.0304291 0.9875755 0.9748922 0.9573003
## [365] 0.9634057 0.9492638 0.9864331 0.9997047 1.0259614 1.0384086 1.0547996
## [372] 1.0510885 1.0290793 0.9857461 0.9735798 0.9566806 0.9636722 0.9499135
## [379] 0.9865238 0.9995052 1.0249483 1.0380684 1.0536343 1.0486103 1.0263256
## [386] 0.9853200 0.9736674 0.9579181 0.9664695 0.9520854 0.9872013 1.0005628
## [393] 1.0249530 1.0380548 1.0527205 1.0483536 1.0256787 0.9842485 0.9730761
## [400] 0.9575856 0.9650732 0.9536601 0.9887414 1.0011364 1.0251652 1.0374148
## [407] 1.0533457 1.0492436 1.0258158
##
##
## Error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set -0.003589237 0.3279793 0.2447078 -0.01558336 0.974642 0.4286617
## ACF1
## Training set 0.113769
##
## Forecasts:
## Point Forecast
## 6.514 25.76427
## 6.528 25.27364
## 6.542 25.10713
## 6.556 25.17508
## 6.569 25.48576
## 6.583 26.09498
## 6.597 26.38883
## 6.611 26.84103
## 6.625 27.15326
## 6.639 27.46529
## 6.653 26.97055
## 6.667 26.51511
## 6.681 25.95479
## 6.694 25.41962
## 6.708 25.09870
## 6.722 24.92979
## 6.736 25.28577
## 6.750 25.91912
## 6.764 26.19231
## 6.778 26.54581
## 6.792 26.94157
## 6.806 27.29538
## 6.819 26.96008
## 6.833 26.40583
## 6.847 25.55935
## 6.861 25.11895
## 6.875 25.03843
## 6.889 24.74309
## 6.903 24.99382
## 6.917 25.35678
## 6.931 26.10075
## 6.944 26.54165
## 6.958 27.01197
## 6.972 27.31504
## 6.986 27.01931
## 7.000 26.42241
## 7.014 25.74988
## 7.028 25.21549
## 7.042 25.16228
## 7.056 25.00241
## 7.069 25.39652
## 7.083 25.76707
## 7.097 26.38554
## 7.111 26.95357
## 7.125 27.47323
## 7.139 27.77144
## 7.153 27.57070
## 7.167 27.09244
## 7.181 26.33333
## 7.194 25.65600ARIMA
arima_training <- window(sst_honolii_1985_2021_month_ts, start = c(1985,11), end = c(2018,11))
fit_arima_training <- auto.arima(arima_training, d = 1, D = 1, stepwise = FALSE, approximation = FALSE, trace = TRUE)##
## ARIMA(0,1,0)(0,1,0)[12] : 337.3851
## ARIMA(0,1,0)(0,1,1)[12] : 152.971
## ARIMA(0,1,0)(0,1,2)[12] : Inf
## ARIMA(0,1,0)(1,1,0)[12] : 267.7923
## ARIMA(0,1,0)(1,1,1)[12] : Inf
## ARIMA(0,1,0)(1,1,2)[12] : Inf
## ARIMA(0,1,0)(2,1,0)[12] : 209.2275
## ARIMA(0,1,0)(2,1,1)[12] : 152.3085
## ARIMA(0,1,0)(2,1,2)[12] : 154.0828
## ARIMA(0,1,1)(0,1,0)[12] : 332.0958
## ARIMA(0,1,1)(0,1,1)[12] : 151.1014
## ARIMA(0,1,1)(0,1,2)[12] : Inf
## ARIMA(0,1,1)(1,1,0)[12] : 266.9615
## ARIMA(0,1,1)(1,1,1)[12] : Inf
## ARIMA(0,1,1)(1,1,2)[12] : Inf
## ARIMA(0,1,1)(2,1,0)[12] : 206.1276
## ARIMA(0,1,1)(2,1,1)[12] : 149.185
## ARIMA(0,1,1)(2,1,2)[12] : 150.8767
## ARIMA(0,1,2)(0,1,0)[12] : 305.7661
## ARIMA(0,1,2)(0,1,1)[12] : 129.3635
## ARIMA(0,1,2)(0,1,2)[12] : Inf
## ARIMA(0,1,2)(1,1,0)[12] : 239.1892
## ARIMA(0,1,2)(1,1,1)[12] : Inf
## ARIMA(0,1,2)(1,1,2)[12] : Inf
## ARIMA(0,1,2)(2,1,0)[12] : 183.469
## ARIMA(0,1,2)(2,1,1)[12] : 128.2519
## ARIMA(0,1,3)(0,1,0)[12] : 304.2383
## ARIMA(0,1,3)(0,1,1)[12] : Inf
## ARIMA(0,1,3)(0,1,2)[12] : Inf
## ARIMA(0,1,3)(1,1,0)[12] : 235.0458
## ARIMA(0,1,3)(1,1,1)[12] : Inf
## ARIMA(0,1,3)(2,1,0)[12] : 180.3435
## ARIMA(0,1,4)(0,1,0)[12] : 302.7527
## ARIMA(0,1,4)(0,1,1)[12] : Inf
## ARIMA(0,1,4)(1,1,0)[12] : 235.934
## ARIMA(0,1,5)(0,1,0)[12] : 304.8166
## ARIMA(1,1,0)(0,1,0)[12] : 335.6048
## ARIMA(1,1,0)(0,1,1)[12] : 152.655
## ARIMA(1,1,0)(0,1,2)[12] : Inf
## ARIMA(1,1,0)(1,1,0)[12] : 268.3383
## ARIMA(1,1,0)(1,1,1)[12] : Inf
## ARIMA(1,1,0)(1,1,2)[12] : Inf
## ARIMA(1,1,0)(2,1,0)[12] : 208.356
## ARIMA(1,1,0)(2,1,1)[12] : 151.2299
## ARIMA(1,1,0)(2,1,2)[12] : 152.9653
## ARIMA(1,1,1)(0,1,0)[12] : Inf
## ARIMA(1,1,1)(0,1,1)[12] : Inf
## ARIMA(1,1,1)(0,1,2)[12] : Inf
## ARIMA(1,1,1)(1,1,0)[12] : Inf
## ARIMA(1,1,1)(1,1,1)[12] : Inf
## ARIMA(1,1,1)(1,1,2)[12] : Inf
## ARIMA(1,1,1)(2,1,0)[12] : 180.4849
## ARIMA(1,1,1)(2,1,1)[12] : 119.6846
## ARIMA(1,1,2)(0,1,0)[12] : Inf
## ARIMA(1,1,2)(0,1,1)[12] : Inf
## ARIMA(1,1,2)(0,1,2)[12] : Inf
## ARIMA(1,1,2)(1,1,0)[12] : 234.2035
## ARIMA(1,1,2)(1,1,1)[12] : Inf
## ARIMA(1,1,2)(2,1,0)[12] : 178.8286
## ARIMA(1,1,3)(0,1,0)[12] : Inf
## ARIMA(1,1,3)(0,1,1)[12] : Inf
## ARIMA(1,1,3)(1,1,0)[12] : 236.247
## ARIMA(1,1,4)(0,1,0)[12] : Inf
## ARIMA(2,1,0)(0,1,0)[12] : 318.0301
## ARIMA(2,1,0)(0,1,1)[12] : 141.5053
## ARIMA(2,1,0)(0,1,2)[12] : Inf
## ARIMA(2,1,0)(1,1,0)[12] : 250.7248
## ARIMA(2,1,0)(1,1,1)[12] : Inf
## ARIMA(2,1,0)(1,1,2)[12] : Inf
## ARIMA(2,1,0)(2,1,0)[12] : 195.1797
## ARIMA(2,1,0)(2,1,1)[12] : 140.5947
## ARIMA(2,1,1)(0,1,0)[12] : Inf
## ARIMA(2,1,1)(0,1,1)[12] : Inf
## ARIMA(2,1,1)(0,1,2)[12] : Inf
## ARIMA(2,1,1)(1,1,0)[12] : 234.5558
## ARIMA(2,1,1)(1,1,1)[12] : Inf
## ARIMA(2,1,1)(2,1,0)[12] : 178.9378
## ARIMA(2,1,2)(0,1,0)[12] : Inf
## ARIMA(2,1,2)(0,1,1)[12] : Inf
## ARIMA(2,1,2)(1,1,0)[12] : Inf
## ARIMA(2,1,3)(0,1,0)[12] : Inf
## ARIMA(3,1,0)(0,1,0)[12] : 311.5994
## ARIMA(3,1,0)(0,1,1)[12] : 133.0865
## ARIMA(3,1,0)(0,1,2)[12] : Inf
## ARIMA(3,1,0)(1,1,0)[12] : 241.1961
## ARIMA(3,1,0)(1,1,1)[12] : Inf
## ARIMA(3,1,0)(2,1,0)[12] : 187.8719
## ARIMA(3,1,1)(0,1,0)[12] : Inf
## ARIMA(3,1,1)(0,1,1)[12] : Inf
## ARIMA(3,1,1)(1,1,0)[12] : Inf
## ARIMA(3,1,2)(0,1,0)[12] : Inf
## ARIMA(4,1,0)(0,1,0)[12] : 306.8487
## ARIMA(4,1,0)(0,1,1)[12] : 128.1566
## ARIMA(4,1,0)(1,1,0)[12] : 237.7129
## ARIMA(4,1,1)(0,1,0)[12] : 308.6511
## ARIMA(5,1,0)(0,1,0)[12] : 308.7123
##
##
##
## Best model: ARIMA(1,1,1)(2,1,1)[12]print(summary(fit_arima_training))## Series: arima_training
## ARIMA(1,1,1)(2,1,1)[12]
##
## Coefficients:
## ar1 ma1 sar1 sar2 sma1
## 0.6682 -0.9087 0.0665 -0.1212 -0.8808
## s.e. 0.0694 0.0431 0.0615 0.0573 0.0412
##
## sigma^2 estimated as 0.07447: log likelihood=-53.73
## AIC=119.46 AICc=119.68 BIC=143.17
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.0009125921 0.266626 0.2000219 -0.002776455 0.7976843 0.522837
## ACF1
## Training set 0.04984805checkresiduals(fit_arima_training)
##
## Ljung-Box test
##
## data: Residuals from ARIMA(1,1,1)(2,1,1)[12]
## Q* = 21.762, df = 19, p-value = 0.2963
##
## Model df: 5. Total lags used: 24fcst_arima <- forecast(fit_arima_training, h = 146, find.frequency = TRUE)## Warning in forecast.forecast_ARIMA(fit_arima_training, h = 146, find.frequency =
## TRUE): The non-existent find.frequency arguments will be ignored.autoplot(fcst_arima, include = 120)
print(summary(fcst_arima))##
## Forecast method: ARIMA(1,1,1)(2,1,1)[12]
##
## Model Information:
## Series: arima_training
## ARIMA(1,1,1)(2,1,1)[12]
##
## Coefficients:
## ar1 ma1 sar1 sar2 sma1
## 0.6682 -0.9087 0.0665 -0.1212 -0.8808
## s.e. 0.0694 0.0431 0.0615 0.0573 0.0412
##
## sigma^2 estimated as 0.07447: log likelihood=-53.73
## AIC=119.46 AICc=119.68 BIC=143.17
##
## Error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.0009125921 0.266626 0.2000219 -0.002776455 0.7976843 0.522837
## ACF1
## Training set 0.04984805
##
## Forecasts:
## Point Forecast Lo 80 Hi 80 Lo 95 Hi 95
## Dec 2018 25.56573 25.21601 25.91546 25.03087 26.10059
## Jan 2019 24.92636 24.48721 25.36552 24.25473 25.59799
## Feb 2019 24.69796 24.21143 25.18449 23.95388 25.44204
## Mar 2019 24.50198 23.98600 25.01797 23.71285 25.29112
## Apr 2019 24.82182 24.28537 25.35827 24.00139 25.64226
## May 2019 25.39745 24.84548 25.94943 24.55328 26.24163
## Jun 2019 25.84813 25.28351 26.41274 24.98463 26.71163
## Jul 2019 26.31908 25.74361 26.89456 25.43896 27.19920
## Aug 2019 26.73995 26.15474 27.32517 25.84495 27.63496
## Sep 2019 27.11226 26.51806 27.70646 26.20350 28.02101
## Oct 2019 26.88556 26.28288 27.48823 25.96384 27.80727
## Nov 2019 26.35828 25.74749 26.96907 25.42416 27.29240
## Dec 2019 25.54700 24.91478 26.17921 24.58011 26.51388
## Jan 2020 24.94836 24.29931 25.59742 23.95572 25.94100
## Feb 2020 24.76535 24.10220 25.42850 23.75115 25.77955
## Mar 2020 24.69589 24.02036 25.37142 23.66276 25.72902
## Apr 2020 24.92559 24.23880 25.61238 23.87523 25.97595
## May 2020 25.43331 24.73601 26.13062 24.36688 26.49975
## Jun 2020 25.92441 25.21711 26.63171 24.84269 27.00613
## Jul 2020 26.40124 25.68433 27.11815 25.30482 27.49766
## Aug 2020 26.84524 26.11900 27.57148 25.73455 27.95593
## Sep 2020 27.13824 26.40289 27.87360 26.01362 28.26287
## Oct 2020 26.87915 26.13486 27.62343 25.74086 28.01743
## Nov 2020 26.34655 25.59348 27.09961 25.19483 27.49826
## Dec 2020 25.52487 24.76260 26.28714 24.35908 26.69066
## Jan 2021 24.92646 24.15525 25.69768 23.74699 26.10593
## Feb 2021 24.76198 23.98202 25.54193 23.56914 25.95481
## Mar 2021 24.67332 23.88479 25.46185 23.46737 25.87928
## Apr 2021 24.96495 24.16798 25.76192 23.74609 26.18381
## May 2021 25.48842 24.68312 26.29372 24.25683 26.72001
## Jun 2021 25.96142 25.14791 26.77494 24.71726 27.20559
## Jul 2021 26.43547 25.61383 27.25711 25.17888 27.69206
## Aug 2021 26.86946 26.03979 27.69914 25.60058 28.13835
## Sep 2021 27.19077 26.35314 28.02841 25.90972 28.47182
## Oct 2021 26.95577 26.11026 27.80128 25.66267 28.24886
## Nov 2021 26.41254 25.55922 27.26585 25.10751 27.71757
## Dec 2021 25.57595 24.70969 26.44221 24.25113 26.90077
## Jan 2022 24.97262 24.09497 25.85028 23.63037 26.31488
## Feb 2022 24.80387 23.91582 25.69192 23.44571 26.16202
## Mar 2022 24.69861 23.80082 25.59639 23.32557 26.07165
## Apr 2022 25.00527 24.09822 25.91233 23.61805 26.39250
## May 2022 25.53802 24.62201 26.45403 24.13711 26.93894
## Jun 2022 26.00492 25.08020 26.92965 24.59068 27.41917
## Jul 2022 26.47807 25.54481 27.41134 25.05078 27.90537
## Aug 2022 26.90860 25.96695 27.85025 25.46847 28.34874
## Sep 2022 27.24140 26.29147 28.19133 25.78861 28.69420
## Oct 2022 27.01193 26.05382 27.97003 25.54663 28.47722
## Nov 2022 26.46863 25.50244 27.43483 24.99097 27.94630
## Dec 2022 25.63232 24.65159 26.61304 24.13243 27.13220
## Jan 2023 25.02863 24.03532 26.02195 23.50949 26.54778
## Feb 2023 24.85735 23.85268 25.86202 23.32085 26.39385
## Mar 2023 24.75331 23.73810 25.76852 23.20069 26.30593
## Apr 2023 25.05347 24.02828 26.07867 23.48557 26.62137
## May 2023 25.58493 24.55013 26.61973 24.00234 27.16752
## Jun 2023 26.05362 25.00949 27.09775 24.45676 27.65047
## Jul 2023 26.52705 25.47380 27.58029 24.91624 28.13785
## Aug 2023 26.95855 25.89634 28.02077 25.33404 28.58307
## Sep 2023 27.28869 26.21764 28.35974 25.65067 28.92671
## Oct 2023 27.05666 25.97689 28.13643 25.40529 28.70803
## Nov 2023 26.51465 25.42624 27.60306 24.85008 28.17923
## Dec 2023 25.68016 24.57743 26.78289 23.99368 27.36664
## Jan 2024 25.07705 23.96170 26.19240 23.37126 26.78284
## Feb 2024 24.90611 23.77923 26.03300 23.18270 26.62953
## Mar 2024 24.80417 23.66649 25.94185 23.06424 26.54410
## Apr 2024 25.10208 23.95409 26.25006 23.34638 26.85777
## May 2024 25.63232 24.47438 26.79026 23.86141 27.40324
## Jun 2024 26.10187 24.93423 27.26951 24.31612 27.88762
## Jul 2024 26.57542 25.39828 27.75257 24.77513 28.37572
## Aug 2024 27.00742 25.82091 28.19393 25.19281 28.82203
## Sep 2024 27.33598 26.14024 28.53173 25.50725 29.16472
## Oct 2024 27.10311 25.89823 28.30800 25.26040 28.94582
## Nov 2024 26.56120 25.34727 27.77513 24.70465 28.41774
## Dec 2024 25.72680 24.49856 26.95503 23.84837 27.60522
## Jan 2025 25.12377 23.88278 26.36475 23.22584 27.02169
## Feb 2025 24.95316 23.70044 26.20589 23.03729 26.86904
## Mar 2025 24.85121 23.58742 26.11499 22.91841 26.78400
## Apr 2025 25.14975 23.87537 26.42414 23.20075 27.09876
## May 2025 25.68008 24.39542 26.96473 23.71536 27.64479
## Jun 2025 26.14946 24.85477 27.44415 24.16941 28.12952
## Jul 2025 26.62299 25.31845 27.92753 24.62787 28.61812
## Aug 2025 27.05490 25.74065 28.36915 25.04492 29.06488
## Sep 2025 27.38368 26.05984 28.70753 25.35904 29.40833
## Oct 2025 27.15107 25.81773 28.48441 25.11190 29.19024
## Nov 2025 26.60900 25.26625 27.95175 24.55545 28.66256
## Dec 2025 25.77438 24.41712 27.13165 23.69863 27.85014
## Jan 2026 25.17129 23.80102 26.54157 23.07564 27.26695
## Feb 2026 25.00067 23.61836 26.38298 22.88661 27.11473
## Mar 2026 24.89846 23.50477 26.29215 22.76699 27.02992
## Apr 2026 25.19732 23.79270 26.60194 23.04913 27.34550
## May 2026 25.72779 24.31256 27.14303 23.56338 27.89221
## Jun 2026 26.19707 24.77145 27.62268 24.01678 28.37736
## Jul 2026 26.67058 25.23476 28.10640 24.47468 28.86647
## Aug 2026 27.10242 25.65654 28.54831 24.89113 29.31371
## Sep 2026 27.43141 25.97558 28.88725 25.20490 29.65792
## Oct 2026 27.19891 25.73322 28.66460 24.95733 29.44049
## Nov 2026 26.65683 25.18137 28.13229 24.40031 28.91335
## Dec 2026 25.82218 24.33200 27.31237 23.54314 28.10122
## Jan 2027 25.21908 23.71561 26.72254 22.91973 27.51842
## Feb 2027 25.04841 23.53261 26.56420 22.73020 27.36662
## Mar 2027 24.94619 23.41869 26.47368 22.61009 27.28228
## Apr 2027 25.24499 23.70624 26.78374 22.89167 27.59831
## May 2027 25.77547 24.22576 27.32517 23.40540 28.14553
## Jun 2027 26.24475 24.68433 27.80517 23.85829 28.63121
## Jul 2027 26.71826 25.14729 28.28924 24.31566 29.12086
## Aug 2027 27.15011 25.56872 28.73150 24.73158 29.56864
## Sep 2027 27.47909 25.88739 29.07078 25.04480 29.91338
## Oct 2027 27.24657 25.64466 28.84847 24.79667 29.69647
## Nov 2027 26.70450 25.09247 28.31653 24.23911 29.16989
## Dec 2027 25.86988 24.24293 27.49683 23.38167 28.35809
## Jan 2028 25.26678 23.62630 26.90726 22.75788 27.77568
## Feb 2028 25.09611 23.44302 26.74921 22.56792 27.62430
## Mar 2028 24.99392 23.32882 26.65901 22.44738 27.54046
## Apr 2028 25.29268 23.61601 26.96935 22.72843 27.85693
## May 2028 25.82314 24.13519 27.51109 23.24165 28.40463
## Jun 2028 26.29244 24.59344 27.99144 23.69404 28.89084
## Jul 2028 26.76595 25.05607 28.47584 24.15091 29.38100
## Aug 2028 27.19781 25.47717 28.91845 24.56631 29.82931
## Sep 2028 27.52676 25.79547 29.25805 24.87898 30.17454
## Oct 2028 27.29422 25.55238 29.03607 24.63031 29.95814
## Nov 2028 26.75216 24.99985 28.50447 24.07223 29.43209
## Dec 2028 25.91754 24.15011 27.68498 23.21449 28.62060
## Jan 2029 25.31445 23.53324 27.09566 22.59032 28.03857
## Feb 2029 25.14378 23.34968 26.93789 22.39994 27.88763
## Mar 2029 25.04159 23.23519 26.84799 22.27894 27.80425
## Apr 2029 25.34036 23.52208 27.15865 22.55953 28.12119
## May 2029 25.87082 24.04094 27.70069 23.07226 28.66937
## Jun 2029 26.34012 24.49886 28.18137 23.52416 29.15607
## Jul 2029 26.81363 24.96116 28.66610 23.98053 29.64674
## Aug 2029 27.24549 25.38194 29.10904 24.39543 30.09555
## Sep 2029 27.57444 25.69991 29.44897 24.70759 30.44129
## Oct 2029 27.34190 25.45649 29.22732 24.45841 30.22540
## Nov 2029 26.79984 24.90361 28.69606 23.89981 29.69986
## Dec 2029 25.96522 24.05367 27.87677 23.04176 28.88868
## Jan 2030 25.36212 23.43655 27.28770 22.41721 28.30703
## Feb 2030 25.19146 23.25272 27.13020 22.22641 28.15651
## Mar 2030 25.08927 23.13794 27.04060 22.10496 28.07357
## Apr 2030 25.38804 23.42452 27.35156 22.38510 28.39098
## May 2030 25.91850 23.94308 27.89392 22.89735 28.93964
## Jun 2030 26.38779 24.40068 28.37490 23.34877 29.42682
## Jul 2030 26.86131 24.86266 28.85995 23.80465 29.91797
## Aug 2030 27.29316 25.28312 29.30321 24.21906 30.36727
## Sep 2030 27.62212 25.60077 29.64347 24.53073 30.71351
## Oct 2030 27.38959 25.35702 29.42215 24.28105 30.49812
## Nov 2030 26.84752 24.80382 28.89121 23.72195 29.97308
## Dec 2030 26.01290 23.95367 28.07213 22.86358 29.16222
## Jan 2031 25.40980 23.33630 27.48330 22.23866 28.58095sst_honolii_2014_2021_month <- sst_honolii_1985_2021_month %>%
filter(time > '2015-11-08')
sst_honolii_2014_2021_month_plot <- ggplot(data = sst_honolii_2014_2021_month, aes(x = time, y = mean_sst)) +
geom_line()
sst_honolii_1985_2021_month_plot
sst_honolii_2014_2021_month_regression <- sst_honolii_2014_2021_month_plot +
geom_smooth(method="lm", col="black")
sst_honolii_2014_2021_month_regression## `geom_smooth()` using formula 'y ~ x'